181–190 of 828 posts
If current AI is good enough to teach you something, spending time learning that thing seems to be a really bad investment...
How does that make sense? So you'd learn it if it was bad at teaching? Do you apply the same principle with humans and not bother to learn if the teacher is good?
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You should always check. I've seen LLM's being wrong (and obstinate) on topics which are one step separated from common knowledge. I had to post the source code to win the dispute, so to speak.
Why would you try to convince an LLM of anything?
If you are curious it was a question about the behavior of Kafka producer interceptors when an exception is thrown.
But I agree that it is hard to resist the temptation to treat LLM's as a pear.
I'll personally attest: LLM's have been absolutely incredible to self learn new things post graduation. It used to be that if you got stuck on a concept, you're basically screwed. Unless it was common enough to show up in a well formed question on stack exchange, it was pretty much impossible, and the only thing you can really do is keep paving forward and hope at some point, it'll make sense to you. Now, everyone ba…
> It used to be that if you got stuck on a concept, you're basically screwed No, not really. > Unless it was common enough to show up in a well formed question on stack exchange, it was pretty much impossible, and the only thing you can really do is keep paving forward and hope at some point, it'll make sense to you. Your experience isn't universal. Some students learned how to do research in school.
I'll personally attest: LLM's have been absolutely incredible to self learn new things post graduation. It used to be that if you got stuck on a concept, you're basically screwed. Unless it was common enough to show up in a well formed question on stack exchange, it was pretty much impossible, and the only thing you can really do is keep paving forward and hope at some point, it'll make sense to you. Now, everyone ba…
I'll personally attest: LLM's have been absolutely incredible to self learn new things post graduation. It used to be that if you got stuck on a concept, you're basically screwed. Unless it was common enough to show up in a well formed question on stack exchange, it was pretty much impossible, and the only thing you can really do is keep paving forward and hope at some point, it'll make sense to you. Now, everyone ba…
>Now, everyone basically has a personal TA, ready to go at all hours of the day This simply hasn't been my experience. Its too shallow. The deeper I go, the less it seems to be useful. This happens quick for me. Also, god forbid you're researching a complex and possibly controversial subject and you want it to find reputable sources or particularly academic ones.
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> It's not my experience that there are missing pieces as compared to anything else. How can you know this when you are learning something? It seems like a confirmation bias to even have this opinion?
I'd gently point out we're 4 questions into "what about if you went about it stupidly and actually learned nothing?" It's entirely possible they learned nothing and they're missing huge parts. But we're sort of at the point where in order to ignore their self-reported experience, we're asking philosophical questions that amount to "how can you know you know if you don't know what you don't know and definitely don't k…
I was attempting to imply that with high-quality literature, it is often reviewed by humans who have some sort of knowledge about a particular topic or are willing to cross reference it with existing literature. The reader often does this as well.
For low-effort literature, this is often not the case, and can lead to things like https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect where a trained observer can point out that something is wrong, but an untrained observer cannot perceive what is incorrect.
IMO, this is adjacent to what human agents interacting with language models experience often. It isn't wrong about everything, but the nuance is enough to introduce some poor underlying thought patterns while learning.
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Same way you know for humans?
But an LLM isn't a human, with a human you can read body language or look up their past body of work. How do you do his with against an LLM
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how are you checking its correctness if you're learning the topic?
This is important, as benchmarks indicate we aren't at a level where a LLM can truly be relied upon to teach topics across the board. It is hard to verify information that you are unfamiliar with. It would be like learning from a message board. Can you really trust what is being said?
So what if the LLM is wrong about something. Human teachers are wrong about things, you are wrong about things, I am wrong about things. We figure it out when it doesn't work the way we thought and adjust our thinking. We aren't learning how to operate experimental nuclear reactors here, where messing up results in half a country getting irradiated. We are learning things for fun, hobbies, and self-betterment.